RT Journal Article
SR Electronic
T1 Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 497834
DO 10.1101/497834
A1 Mohammad Tanhaemami
A1 Elaheh Alizadeh
A1 Claire K. Sanders
A1 Babetta L. Marrone
A1 Brian Munsky
YR 2019
UL http://biorxiv.org/content/early/2019/06/04/497834.abstract
AB Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.